An Efficient Scheduling Algorithm for Sensor-Based IoT Networks

Internet of Things (IoT) based networks with sensors are energy and delay stringent. Efficient scheduling algorithms for IoT-based networks are the need of the hour. Nodes with selfish behavior degrade the performance of the network. Hence, a scheduling algorithm that schedules packets based on their emergencies and priorities yields better results. In this paper, M/M/1 and M/M/N scheduling scheme to schedule Emergency packets (E-packets) and Regular packets (R-packets) is proposed. The next-hop nodes are chosen based on the trust value of nodes. It is seen that the proposed scheme yields better results in terms of Packet Delivery Ratio (PDR), end-to-end delay, throughput and routing overhead.

[1]  Hamid Gharavi,et al.  Greedy backpressure routing for Smart Grid sensor networks , 2014, 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC).

[2]  Pinaki Sankar Chatterjee,et al.  Selfish node detection and its behavior in WSN , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[3]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[4]  Guangjie Han,et al.  Analysis of Energy-Efficient Connected Target Coverage Algorithms for Industrial Wireless Sensor Networks , 2017, IEEE Transactions on Industrial Informatics.

[5]  Donald F. Towsley,et al.  Cluster-Based Back-Pressure Routing Algorithm , 2008, IEEE/ACM Transactions on Networking.

[6]  Athanasios V. Vasilakos,et al.  Backpressure-based routing protocol for DTNs , 2010, SIGCOMM '10.

[7]  Bhaskar Krishnamachari,et al.  Trust-based backpressure routing in wireless sensor networks , 2015, Int. J. Sens. Networks.

[8]  Hao Chen,et al.  A brief introduction to IoT gateway , 2011 .

[9]  Bhaskar Krishnamachari,et al.  LIFO-Backpressure Achieves Near-Optimal Utility-Delay Tradeoff , 2010, IEEE/ACM Transactions on Networking.

[10]  Tie Qiu,et al.  EABS: An Event-Aware Backpressure Scheduling Scheme for Emergency Internet of Things , 2018, IEEE Transactions on Mobile Computing.

[11]  Jianzhong Li,et al.  A Study on Application-Aware Scheduling in Wireless Networks , 2017, IEEE Transactions on Mobile Computing.

[12]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[13]  Bhaskar Krishnamachari,et al.  Routing without routes: the backpressure collection protocol , 2010, IPSN '10.

[14]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[15]  Zheng Yao,et al.  A distributed gradient-assisted anycast-based backpressure framework for wireless sensor networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[16]  Ness B. Shroff,et al.  Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks , 2011, IEEE/ACM Transactions on Networking.

[17]  Michael J. Neely,et al.  Optimal Backpressure Routing for Wireless Networks with Multi-Receiver Diversity , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[18]  Hamid Mirvaziri,et al.  BDCC: Backpressure routing and dynamic prioritization for congestion control in WMSNs , 2014 .

[19]  Eytan Modiano,et al.  Dynamic power allocation and routing for time-varying wireless networks , 2005 .

[20]  H. T. Mouftah,et al.  A virtual queue-based back-pressure scheduling algorithm for wireless sensor networks , 2015, EURASIP J. Wirel. Commun. Netw..

[21]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..